Solar power prediction in real time utilising supervised machine learning algorithms considering Madhya Pradesh region. (14th June 2022)

• Record Type: Journal Article
• Authors: Jain, Sanjiv Kumar; Yawalkar, Kaustubh; Singh, Prakhar; Apte, Advait
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 3(2022)
• Page Start: 218
• Page End: 227
• Publication Date: 2022-06-14
• Subjects: machine learning -- neural network -- Poisson regression -- random forest -- solar power output -- supervised learning; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Solar energy holds the key for future electric power generation providing sustainable development to meet the requirement for long lasting demand in the future. As exhaustible sources are depleting constantly, we need to focus on non-exhaustible sources. The prediction of solar power output is critical to plan for the future operations and integrating the power grid with renewable sources. In this study, a dataset of climatic parameters that is, beam (direct) irradiance, diffuse irradiance, reflected irradiance, sun height, air temperature and wind speed for five years is used to predict the solar power output based on photovoltaic technology of Indore region (27.2046°N, 77.4977°E). The performance of the trained models is determined using statistical (mathematical) indicators. Amongst the machine learning algorithms, the best accuracy of 98% is achieved by random forest method for the prediction of solar power output for Indore region.